Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/31341

Full metadata record

DC Field

Value

Language

dc.contributor.author

Cameron, A. Colin

en_US

dc.date.accessioned

2006-03-08

en_US

dc.date.accessioned

2010-05-14T11:04:09Z

-

dc.date.available

2010-05-14T11:04:09Z

-

dc.date.issued

2006

en_US

dc.identifier.uri

http://hdl.handle.net/10419/31341

-

dc.description.abstract

A very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and conditional logit, nested logit, multinomial probit, and random parameters logit. The last two models are estimated using simulation or Bayesian methods. For ordered data, standard multinomial models are ordered logit and probit, or count models are used if ordered discrete data are actually a count.

en_US

dc.language.iso

eng

en_US

dc.publisher

|aDep. of Economics, Univ. of California |cDavis, Calif.

en_US

dc.relation.ispartofseries

|aWorking papers // University of California, Department of Economics |x06,12